A statistically-driven Coral Reef Optimization algorithm for optimal size reduction of time series

Áreas de investigación:

• Methodology - Machine learning

Año:

2018

Tipo de publicación:

Artículo

Autores:

• Durán-Rosal, Antonio Manuel
• Gutiérrez, Pedro Antonio
• Salcedo-Sanz, Sancho
• Hervás-Martínez, César

Journal:

Applied Soft Computing

Volumen:

63

Páginas:

139-153

ISSN:

1568-4946

BibTex:

@article{SCRO2018, author = "Antonio Manuel Dur{\'a}n-Rosal and Pedro Antonio Guti{\'e}rrez and Sancho Salcedo-Sanz and C{\'e}sar Herv{\'a}s-Mart{\'i}nez", abstract = "This paper is focused on reducing the number of elements in time series with minimum information loss, with specific applications on time series segmentation. A modification of the coral reefs optimization metaheuristic (CRO) is proposed for this purpose, which is called statistical CRO (SCRO), where the main parameters of the algorithm are adjusted based on the mean and standard deviation associated with the fitness distribution. Moreover, the algorithm is combined with the Bottom-Up and Top-Down methodologies (traditional local search methods for time series segmentation), resulting in a hybrid methodology (HSCRO). We evaluate the performance of these algorithms using 16 time series from different application areas. The statistically-driven version of CRO is shown to improve the results of the standard CRO, eliminating the necessity of manually adjusting the main parameters of the algorithm and dynamically adjusting these parameters throughout the evolution. Moreover, when compared with other local search methods and metaheuristics from the state of the art, HSCRO shows robust segmentation results, consistently obtaining lower approximation errors.", awards = "JCR(2018): 4.873 Position: 20/134 (Q1) Category: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE.", comments = "JCR(2018): 4.873 Position: 20/134 (Q1) Category: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE.", doi = "10.1016/j.asoc.2017.11.037", issn = "1568-4946", journal = "Applied Soft Computing", note = "JCR(2018): 4.873 Position: 20/134 (Q1) Category: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE.", pages = "139-153", title = "{A} statistically-driven {C}oral {R}eef {O}ptimization algorithm for optimal size reduction of time series", url = "doi.org/10.1016/j.asoc.2017.11.037", volume = "63", year = "2018", }

Nota:

JCR(2018): 4.873 Position: 20/134 (Q1) Category: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE.

Abstract:

This paper is focused on reducing the number of elements in time series with minimum information loss, with specific applications on time series segmentation. A modification of the coral reefs optimization metaheuristic (CRO) is proposed for this purpose, which is called statistical CRO (SCRO), where the main parameters of the algorithm are adjusted based on the mean and standard deviation associated with the fitness distribution. Moreover, the algorithm is combined with the Bottom-Up and Top-Down methodologies (traditional local search methods for time series segmentation), resulting in a hybrid methodology (HSCRO). We evaluate the performance of these algorithms using 16 time series from different application areas. The statistically-driven version of CRO is shown to improve the results of the standard CRO, eliminating the necessity of manually adjusting the main parameters of the algorithm and dynamically adjusting these parameters throughout the evolution. Moreover, when compared with other local search methods and metaheuristics from the state of the art, HSCRO shows robust segmentation results, consistently obtaining lower approximation errors.

Comentarios:

JCR(2018): 4.873 Position: 20/134 (Q1) Category: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE.